基于神经网络的文本分类方法研究

海量文本分析是实现大数据理解和价值发现的重要手段,其中文本分类作为自然语言处理的经典问题受到研究者广泛关注,而人工神经网络在文本分析方面的优异表现使其成为目前的主要研究方向。在此背景下,介绍卷积神经网络、时间递归神经网络、结构递归神经网络和预训练模型等主流方法在文本分类中应用的发展历程,比较不同模型基于常用数据集的分类效果,表明利用人工神经网络结构自动获取文本特征,可避免繁杂的人工特征工程,使文本分类效果得到提升。在此基础上,对未来文本分类的研究方向进行展望。

Research on Text Classification Methods Based on Neural Network

Large-scale text analysis is an important means of understanding and finding value of big data.Hence text classification,as a classical natural language processing problem,has been widely concerned by researchers,and its main research direction is artificial neural network due to its excellent performance in text analysis.This paper introduces the history of Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),recursive neural network structure and the pretraining model applied to text classification.Then this paper compares classification performance of different models based on the common dataset,demonstrating that artificial neural network structure can reduce manual characterization work by automatically obtaining text features,and thus improve text classification effects.On this basis,this paper prospects the future research directions of text classification.